Kajal De

Fuzzy Support Vector Machine for bankruptcy prediction

Bankruptcy prediction has been a topic of active research for business and corporate organizations since past few decades. The problem has been tackled using various models viz., Statistical, Market Based and Computational Intelligence in the past. Among Computational Intelligence models, Artificial Neural Network has become dominant modeling paradigm. In this Paper, we use a novel Soft Computing tool viz., Fuzzy Support Vector Machine (FSVM) to solve bankruptcy prediction problem. Support Vector Machine is a powerful statistical classification technique based on the idea of Structural Risk Minimization. Fuzzy Sets are capable of handling uncertainty and impreciseness in corporate data. Thus, using the advantage of Machine Learning and Fuzzy Sets prediction accuracy of whole model is enhanced. FSVM is implemented for analyzing predictors as financial ratios. A method of adapting it to default probability estimation is proposed. The test dataset comprises of 50 largest bankrupt organizations with capitalization of no less than

A Comparative Study of Kernels for the Multi-class Support Vector Machine

1 billion that filed for protection against creditors under Chapter 11 of United States Bankruptcy Code in 2001-2002 after stock marked crash of 2000. Experimental results on FSVM illustrate that it is better capable of extracting useful information from corporate data. This is followed by a comparative study of FSVM with other approaches. FSVM is effective in finding optimal feature subset and parameters. This is evident from the results thus improving prediction of bankruptcy. The choice of feature subset has positive influence on appropriate kernel parameters and vice versa which demonstrate its appreciable generalization performance than traditional bankruptcy prediction methods. Choosing appropriate value of parameter plays an important role on the performance of FSVM model. The effect of variability in prediction performance of FSVM with respect to various values of different parameters of SVM is also investigated. Finally, a comparative study of clustering power of FSVM is made with PNN on ripley and bankruptcy datasets. The results show that FSVM has superior clustering power than PNN.

A Study of the Traveling Salesman Problem Using Fuzzy Self Organizing Map

Support Vector Machine (SVM) is a powerful classification technique based on the idea of structural risk minimization. Use of a kernel function enables the curse of dimensionality to be addressed. However, a proper kernel function for a certain problem is dependent on the specific dataset and as such there is no good method on how to choose a kernel function. In this paper, the choice of the kernel function is studied empirically and optimal results are achieved for multiclass SVMs combining several binary classifiers. The performance of the Multi-class SVM is illustrated by extensive experimental results which indicate that with suitable Kernel and parameters better classification accuracy can be achieved as compared to other methods. The experimental results of the four datasets show that Gaussian Kernel is not always the best choice to achieve high generalization of classifier although it is often the default choice.

Time Series Forecasting Using Hybrid Neuro-Fuzzy Regression Model

Kohonen self organizing map is an important artificial neural network technique that uses competitive, unsupervised learning to produce a low-dimensional discretized representation of the input space of the training samples which preserves the topological properties of the input space. The fuzzy set theory introduces the concept of membership function to the learning process of Self Organizing Map which helps to handle the inherent vagueness involved in most of the real life problems. In this paper, fuzzy self organizing map with one dimensional neighborhood is used to find an optimal solution for the symmetrical Traveling Salesperson Problem. The solution generated by the Fuzzy Self Organizing Map algorithm is improved by the 2opt algorithm. Finally, the Fuzzy Self Organizing Map algorithm is compared with Lin-Kerninghan Algorithm and Evolutionary Algorithm with Enhanced Edge Recombination operator and self-adapting mutation rate.

Fuzzy Genetic Heuristic for University Course Timetable Problem

During the past few decades various time-series forecasting methods have been developed for financial market forecasting leading to improved decisions and investments. But accuracy remains a matter of concern in these forecasts. The quest is thus on improving the effectiveness of time-series models. Artificial neural networks (ANN) are flexible computing paradigms and universal approximations that have been applied to a wide range of forecasting problems with high degree of accuracy. However, they need large amount of historical data to yield accurate results. The real world situation experiences uncertain and quick changes, as a result of which future situations should be forecasted using small amount of data from a short span of time. Therefore, forecasting in these situations requires techniques that work efficiently with incomplete data for which Fuzzy sets are ideally suitable. In this work, a hybrid Neuro-Fuzzy model combining the advantages of ANN and Fuzzy regression is developed to forecast the exchange rate of US Dollar to Indian Rupee. The model yields more accurate results with fewer observations and incomplete data sets for both point and interval forecasts. The empirical results indicate that performance of the model is comparatively better than other models which make it an ideal candidate for forecasting and decision making.